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Simple Summary: In recent years, advanced radiotherapy techniques such as intensity-modulated radiation therapy (IMRT) and simultaneous integrated boost (SIB) irradiation have played a key role in enhancing the precision of radiation delivery to tumors. However, these advancements have also increased the complexity of treatment planning by involving a trial-and-error approach, resulting in greater variability between operators and longer planning times. The automation of planning processes has shown promise in standardizing treatment plans while maintaining their quality and reducing workload. Additionally, deep learning-based fully automated planning solutions have become a significant focus of research in radiation oncology. In this study, we propose a single, end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments, requiring minimal human input and producing a machine-deliverable volumetric modulated arc therapy (VMAT) plan. A comprehensive clinical evaluation was performed, incorporating both dosimetric analysis and plan deliverability assessment. Background/Objectives: To evaluate an end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments that requires minimal human input and generates a machine-deliverable plan as an output. Methods: In collaboration with TheraPanacea, a treatment planning pipeline was developed that takes as its input a planning CT with organs-at-risk (OARs) and planning target volume (PTV) contours, the targeted linac machine, and the prescription dose. The primary components are (i) dose prediction by a single deep learning model for both localizations and (ii) a direct aperture VMAT plan optimization that seeks to mimic the predicted dose. The deep learning model was trained on 238 cases, and a held-out set of 86 cases was used for model validation. An end-to-end clinical evaluation study was performed on another 40 cases (20 prostate-only, 20 whole-pelvic). First, a quantitative evaluation was performed based on dose–volume histogram (DVH) points and plan parameter metrics. Then, the plan deliverability was assessed via portal dosimetry using the global gamma index. Additionally, the reference clinical manual plans were compared with the automated plans in terms of monitor unit (MU) numbers and modulation complexity scores (MCSv). Results: The automated plans provided adequate treatment plans (or minor deviations) with respect to the dose constraints, and the quality of the plans was similar to the manual plans for both localizations. Moreover, the automated plans showed successful deliverability and passed the portal dose verification. Despite higher median total MUs, no statistically significant correlation was observed between any of the gamma criteria tested and the number of MUs or MCSv. Conclusions: This study shows the feasibility of a deep learning-based fully automated treatment planning pipeline that generates high-quality plans that are competitive with manually made plans and are clinically approved in terms of dosimetry and machine deliverability. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to introduce modified fixed field volumetric modulated arc therapy (MF‐VMAT) which manually opened the field size by fixing the jaws and comparing it to the typical planning technique, auto field volumetric modulated arc therapy (AF‐VMAT) in cervical cancer treatment planning. Methods and materials: Previously treated twenty‐eight cervical cancer plans were retrospectively randomly selected and replanned in this study using two different planning techniques: AF‐VMAT and MF‐VMAT, resulting in a total of fifty‐six treatment plans. In this study, we compared both planning techniques in three parts: (1) Organ at Risk (OARs) and whole‐body dose, (2) Treatment plan efficiency, and (3) Treatment plan accuracy. Results: For OARs dose, bowel bag (p‐value = 0.001), rectum (p‐value = 0.002), and left femoral head (p‐value = 0.001) and whole‐body (p‐value = 0.000) received a statistically significant dose reduction when using the MF‐VMAT plan. Regarding plan efficiency, MF‐VMAT exhibited a statistically significant increase in both number of monitor units (MUs) and control points (p‐values = 0.000), while beam‐on time, maximum leaf travel, average maximum leaf travel, and maximum leaf travel per gantry rotation were statistically significant decreased (p‐values = 0.000). In terms of plan accuracy, the average gamma passing rate was higher in the MF‐VMAT plan for both absolute dose (AD) (p‐value = 0.001, 0.004) and relative dose (RD) (p‐value = 0.000, 0.000) for 3%/3 and 3%/2 mm gamma criteria, respectively. Conclusion: The MF‐VMAT planning technique significantly reduces OAR doses and decreases the spread of low doses to normal tissues in cervical cancer patients. Additionally, this planning approach demonstrates efficient plans with lower beam‐on time and reduced maximum leaf travel. Furthermore, it indicates higher plan accuracy through an increase in the average gamma passing rate compared to the AF‐VMAT plan. Consequently, MF‐VMAT offers an effective treatment planning technique for cervical cancer patients. [ABSTRACT FROM AUTHOR]

Background/Aim: The aim was to assess the complexity of breast volumetric-modulated arc therapy (VMAT) plans using various indices and to evaluate their performance through gamma analysis in predicting plan deliverability. Materials and Methods: A total of 285 VMAT plans for 260 patients were created using the VersaHD™ linear accelerator with a Monaco treatment planning system. Corresponding verification plans were generated using the ArcCHECK® detector, and gamma analysis was conducted employing various criteria. Twenty-eight plan complexity metrics were computed, and Pearson's correlation coefficients were determined between the gamma passing rate (GPR) and these metrics. Results: The average GPR values for all plans were 97.7%, 89.9%, and 78.0% for the 2 mm/2%, 1 mm/2%, and 1 mm/1% criteria, respectively. While most complexity metrics exhibited weak correlations with GPRs under the 2 mm/2% criterion, leaf sequence variability (LSV), plan-averaged beam area (PA), converted area metric (CAM), and edge area metric (EAM) demonstrated the most robust performance, with Pearson’s correlation coefficients of 0.57, 0.50, −0.70, and −0.56, respectively. Conclusion: Metrics related to beam aperture size and irregularity, such as LSV, PA, CAM and EAM, proved to be reasonable predictors of plan deliverability in breast VMAT. [ABSTRACT FROM AUTHOR]

Purpose: SRS MapCHECK (SMC) is a commercially available patient‐specific quality assurance (PSQA) tool for stereotactic radiosurgery (SRS) applications. This study investigates the effects of degree of modulation, location off‐axis, and low dose threshold (LDT) selection on gamma pass rates (GPRs) between SMC and treatment planning system, Analytical Anisotropic Algorithm (AAA), or Vancouver Island Monte Carlo (VMC++ algorithm) system calculated dose distributions. Methods: Volumetric‐modulated arc therapy (VMAT) plans with modulation factors (MFs) ranging from 2.7 to 10.2 MU/cGy were delivered to SMC at isocenter and 6 cm off‐axis. SMC measured dose distributions were compared against AAA and VMC++ via gamma analysis (3%/1 mm) with LDT of 10% to 80% using SNC Patient software. Results: Comparing on‐axis SMC dose against AAA and VMC++ with LDT of 10%, all AAA‐calculated plans met the acceptance criteria of GPR ≥ 90%, and only one VMC++ calculated plan was marginally outside the acceptance criteria with pass rate of 89.1%. Using LDT of 80% revealed decreasing GPR with increasing MF. For AAA, GPRs reduced from 100% at MF of 2.7 MU/cGy to 57% at MF of 10.2 MU/cGy, and for VMC++ calculated plans, the GPRs reduced from 89% to 60% in the same MF range. Comparison of SMC dose off‐axis against AAA and VMC++ showed more pronounced reduction of GPR with increasing MF. For LDT of 10%, AAA GPRs reduced from 100% to 83% in the MF range of 2.7 to 9.8 MU/cGy, and VMC++ GPR reduced from 100% to 91% in the same range. With 80% LDT, GPRs dropped from 100% to 42% for both algorithms. Conclusions: MF, dose calculation algorithm, and LDT selections are vital in VMAT‐based SRT PSQA. LDT of 80% enhances sensitivity of gamma analysis for detecting dose differences compared to 10% LDT. To achieve better agreement between calculated and SMC dose, it is recommended to limit the MF to 4.6 MU/cGy on‐axis and 3.6 MU/cGy off‐axis. [ABSTRACT FROM AUTHOR]

Purpose: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery. Methods: A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database. Multi‐correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates. Results: The MLC positional deviation and effective impact factors show a strong multi‐correlation (R = 0.701, p‐value < 0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5‐fold cross‐validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235 cGy, while the relative volume deviation remained small at 0.470%–5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of −0.506 to −0.720 and p‐value < 0.05) but marginally with dosage deviations (with correlation coefficient < 0.498 and p‐value > 0.05). Conclusions: The RF predictive model provides a prior tool for VMAT quality assurance. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. Methods: The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis. Results: The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and −0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five‐fold cross‐validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365. Conclusions: The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly. [ABSTRACT FROM AUTHOR]

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA. [ABSTRACT FROM AUTHOR]

Background: For patient‐specific quality assurance (PSQA) for small targets, the dose resolution can change depending on the characteristics of the dose calculation algorithms. Purpose: This study aimed to evaluate the influence of the dose calculation algorithms Acuros XB (AXB), anisotropic analytical algorithm (AAA), photon Monte Carlo (pMC), and collapsed cone (CC) on a helical diode array using volumetric‐modulated arc therapy (VMAT) for small targets. Materials and methods: ArcCHECK detectors were inserted with a physical depth of 2.9 cm from the surface. To evaluate the influence of the dose calculation algorithms for small targets, rectangular fields of 2×100, 5×100, 10×100, 20×100, 50×100, and 100×100 mm2 were irradiated and measured using ArcCHECK with TrueBeam STx. A total of 20 VMAT plans for small targets, including the clinical sites of 19 brain metastases and one spine, were also evaluated. The gamma passing rates (GPRs) were evaluated for the rectangular fields and the 20 VMAT plans using AXB, AAA, pMC, and CC. Results: For rectangular fields of 2×100 and 5×100 mm2, the GPR at 3%/2 mm of AXB was < 50% because AXB resulted in a coarser dose resolution with narrow beams. For field sizes > 10×100 mm2, the GPR at 3%/2 mm was > 88.1% and comparable for all dose calculation algorithms. For the 20 VMAT plans, the GPRs at 3%/2 mm were 79.1 ± 15.7%, 93.2 ± 5.8%, 94.9 ± 4.1%, and 94.5 ± 4.1% for AXB, AAA, pMC, and CC, respectively. Conclusion: The behavior of the dose distribution on the helical diode array differed depending on the dose calculation algorithm for small targets. Measurements using ArcCHECK for VMAT with small targets can have lower GPRs owing to the coarse dose resolution of AXB around the detector area. [ABSTRACT FROM AUTHOR]

Backgroud and objectives: The implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model's performance was compared by evaluating the impact of two distinct feature selection methods. Methods: A retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, y analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model's classification performance was assessed through F1-score and area under the curve (AUC) values. Results: The model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible. Conclusion: Based on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists. [ABSTRACT FROM AUTHOR]

The treatment of head and neck cancers (HNCs) encompasses a complex paradigm involving a combination of surgery, radiotherapy, and systemic treatment. Locoregional recurrence is a common cause of treatment failure, and few patients are suitable for salvage surgery. Reirradiation with conventional radiation techniques is challenging due to normal tissue tolerance limits and the risk of significant toxicities. Stereotactic body radiotherapy (SBRT) has emerged as a highly conformal modality that offers the potential for cure while limiting the dose to surrounding tissue. There is also growing research that shows that those with oligometastatic disease can benefit from curative intent local ablative therapies such as SBRT. This review will look at published evidence regarding the use of SBRT in locoregional recurrent and oligometastatic HNCs. [ABSTRACT FROM AUTHOR]

Purpose: For individual targets of single isocenter multi‐target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre‐treatment Quality Assurance (QA) measurement. Methods: Treatment plans from 31 SIMT SRS patients were recalculated in a series of scenarios designed to investigate sources of discrepancy between TPS and independent MC. Targets with > 5% discrepancy in DMean[Gy] after progressing through all scenarios were measured with SRS MapCHECK. A matched pair analysis was performed comparing SRS MapCHECK results for these targets with matched targets having similar characteristics (volume & distance from isocenter) but no such MC dose discrepancy. Results: Of 217 targets analyzed, individual target mean dose (DMean[Gy]) fell outside a 5% threshold for 28 and 24 targets before and after removing tissue heterogeneity effects, respectively, while only 5 exceeded the threshold after removing effect of patient geometry (via calculation on StereoPHAN geometry). Significant factors affecting agreement between the TPS and MC included target distance from isocenter (0.83% decrease in DMean[Gy] per 2 cm), volume (0.15% increase per cc), and degree of plan modulation (0.37% increase per 0.01 increase in modulation complexity score). SRS MapCHECK measurement had better agreement with MC than with TPS (2%/1 mm / 10% threshold gamma pass rate (GPR) = 99.4 ± 1.9% vs. 93.1 ± 13.9%, respectively). In the matched pair analysis, targets exceeding 5% for MC versus TPS also had larger discrepancies between TPS and measurement with no GPR (2%/1 mm / 10% threshold) exceeding 90% (71.5% ± 16.1%); whereas GPR was high for matched targets with no such MC versus TPS difference (96.5% ± 3.3%, p = 0.01). Conclusions: Independent MC complements pre‐treatment QA measurement for SIMT SRS by identifying problematic individual targets prior to pre‐treatment measurement, thus enabling plan modifications earlier in the planning process and guiding selection of targets for pre‐treatment QA measurement. [ABSTRACT FROM AUTHOR]

Purpose: To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy. Methods: Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non‐small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th‐percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R‐squared values were used to determine the best metric. Results: Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites. Conclusion: Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers., Materials and Methods: The plans were first generated using automated RapidPlan with scripting application programming interface (API) and then further refined through manual optimization (RP+MP) to improve plan quality. The quality of 20 plans was assessed, taking into account dose statistics and clinical plan acceptability. The impact of beam complexity on mechanical performance was analyzed using parameters such as leaf speed (LS), leaf acceleration (LA), mean-field area (MFA), cross-axis score (CAS), closed leaf score (CLS), small aperture score (SAS), and monitor units per control point (MU/CP). Patient-specific quality assurance (PSQA) was conducted to determine differences between the RP+MP and original plans., Results: No differences in the heterogeneity index and conformity number were observed between the RP+MP and original plans. The RP+MP plan was superior to the original plan for sparing the left cochlea, left and right internal auditory canals, chiasm, and left optic nerve. Significant differences (p < 0.05) were identified in CAS, SAS 1   mm , SAS 2   mm , and SAS 10mm . However, there was no significant difference in PSQA between the RP+MP and original plans. The RP+MP plan without any modifications was clinically acceptable in 45% of cases., Conclusion: The automated RP with scripting API followed by MP (RP+MP) yielded a high-quality plan in terms of dose statistics and clinical acceptability. The RP+MP plan yielded a higher CAS and SAS compared with the original plan. Nevertheless, there was no significant difference in PSQA between the RP+MP and original plans., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: This study aimed to assess the dosimetric advantages of dual arc increments in head and neck volumetric-modulated arc therapy (VMAT) in the Monaco treatment planning system (TPS)., Methods: Three VMAT plans were created for each of the 10 patients by prescribing 70 Gy in 35 fractions with arc increment combinations of 30°/30°, 15°/15°, and 30°/15° in the Monaco TPS. The dose to 0.03 cm 3 volume (D 0.03cc ), conformity number, and homogeneity and gradient indices was compared for the planning target volume (PTV), and the D 0.03cc and mean dose (D mean ) of the spinal cord, brain stem, parotid glands were compared. For plan complexity evaluation, the monitor unit and various related metrics were compared. Wilcoxon signed-rank tests were performed across plans for the evaluated indicators., Results: For PTV, plans with 30°/15° showed comparable D 0.03cc and homogeneity and gradient indices to those of plans with 30°/30° and 15°/15° while exhibiting a better conformity number. The D 0.03cc for spinal cord and brain stem for plans with 30°/15° were 26.0% and 20.8% less than those with 30°/30° and 16.8% and 19.0% less than those with 15°/15°, respectively. The D mean for the left and right parotid glands under plans with 30°/15° were 17.4% and 13.2% less than those with 30°/30° and 14.0% and 9.8% less than those with 15°/15°, respectively. The total monitor unit in plans with 30°/15° was less than that in other plans but with no significance. The plans with 30°/15° showed higher modulation complexity and plan-averaged irregularity, while no significant differences observed in both plan-averaged area and modulation compared with other plans., Conclusion: In head and neck VMAT, a dual arc increment of 30°/15° seems advisable because it can substantially reduce doses to normal tissues with comparable delivery efficiency while maintaining target dose coverage., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: The purpose of this study was to develop a predictive model to evaluate pretreatment patient-specific quality assurance (QA) based on treatment planning parameters for stereotactic body radiation therapy (SBRT) for liver carcinoma. Materials and Methods: We retrospectively selected 180 cases of liver SBRT treated using the volumetric modulated arc therapy technique. Numerous parameters defining the plan complexity were calculated from the DICOM-RP (Radiotherapy Plan) file using an in-house program developed in MATLAB. Patient-specific QA was performed with global gamma evaluation criteria of 2%/2 mm and 3%/3 mm in a relative mode using the Octavius two-dimensional detector array. Various statistical tests and multivariate predictive models were evaluated. Results: The leaf speed (MILS) and planning target volume size showed the highest correlation with the gamma criteria of 2%/2 mm and 3%/3 mm (P < 0.05). Degree of modulation (DoM), MCSSPORT, leaf speed (MILS), and gantry speed (MIGS) were predictors of global gamma pass rate (GPR) for 2%/2 mm (G22), whereas DoM, MCSSPORT, leaf speed (MILS) and robust decision making were predictors of the global GPR criterion of 3%/3 mm (G33). The variance inflation factor values of all predictors were <2, indicating that the data were not associated with each other. For the G22 prediction, the sensitivity and specificity of the model were 75.0% and 75.0%, respectively, whereas, for G33 prediction, the sensitivity and specificity of the model were 74.9% and 85.7%%, respectively. Conclusions: The model was potentially beneficial as an easy alternative to pretreatment QA in predicting the uncertainty in plan deliverability at the planning stage and could help reduce resources in busy clinics. [ABSTRACT FROM AUTHOR]

Optimizing the positional accuracy of multileaf collimators (MLC) for radiotherapy is important for dose accuracy and for reducing doses delivered to normal tissues. This study investigates dose sensitivity variations and complexity metrics of MLC positional error in volumetric modulated arc therapy and determines the acceptable ranges of MLC positional accuracy in several clinical situations. Treatment plans were generated for four treatment sites (prostate cancer, lung cancer, spinal, and brain metastases) using different treatment planning systems (TPSs) and fraction sizes. Each treatment plan introduced 0.25–2.0 mm systematic or random MLC leaf bank errors. The generalized equivalent uniform dose (gEUD) sensitivity and complexity metrics (MU/Gy and plan irregularity) were calculated, and the correlation coefficients were assessed. Furthermore, the required tolerances for MLC positional accuracy control were calculated. The gEUD sensitivity showed the highest dependence of systematic positional error on the treatment site, followed by TPS and fraction size. The gEUD sensitivities were 6.7, 4.5, 2.5, and 1.7%/mm for Monaco and 8.9, 6.2, 3.4, and 2.3%/mm (spinal metastasis, lung cancer, prostate cancer, and brain metastasis, respectively) for RayStation. The gEUD sensitivity was strongly correlated with the complexity metrics (r = 0.88–0.93). The minimum allowable positional error for MLC was 0.63, 0.34, 1.02, and 0.28 mm (prostate, lung, brain, and spinal metastasis, respectively). The acceptable range of MLC positional accuracy depends on the treatment site, and an appropriate tolerance should be set for each treatment site with reference to the complexity metric. It is expected to enable easier and more detailed MLC positional accuracy control than before by reducing dose errors to patients at the treatment planning stage and by controlling MLC quality based on complexity metrics, such as MU/Gy. [ABSTRACT FROM AUTHOR]

Purpose: We sought to develop machine learning models to predict the results of patient‐specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose‐evaluation metrics—including the gamma passing rates (GPRs)—and criteria based on the radiomic features of 3D dose distribution in a phantom. Methods: A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose‐evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance‐to‐agreement in 1‐mm and 2‐mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient‐specific QA. The machine learning regression models for predicting the values of the dose‐evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross‐validation in which four‐fold cross‐validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. Results: The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose‐evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. Conclusions: The developed machine learning models showed high performance for predicting dose‐evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics. [ABSTRACT FROM AUTHOR]

This study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman’s rank correlation coefficient (Rs). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge Rs = − 0.608, p < 0.01; TrueBeam Rs = − 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is to investigate the impact of large target offset distances on the dose distribution and gamma passing rate (GPR) in single-isocenter multiple-target stereotactic radiosurgery (SIMT SRS) using volumetric modulated arc therapy (VMAT) with a flattening filter-free (FFF) beam from a linear accelerator. Methods: Two targets with a diameter of 1 cm were offset by "±2, ±4, and ±6 cm from the isocenter in a verification phantom for head SRS (20 Gy/fr). The VMAT plans were created using collimator angles that ensured the two targets did not share a leaf pair from the multi-leaf collimator. To evaluate the low-dose spread intermediate dose spill (R50%), GPRs were measured with a criterion of 3%/2 mm using an electronic portal imaging device and evaluated using monitor unit (MU), modulation complexity score for VMAT (MCSv), and leaf travel (LT) parameters. Results: For offsets of 2, 4, and 6 cm, the respective parameters were: R50%, 4.75 ± 0.36, 5.13 ± 0.36, and 5.11 ± 0.33; GPR, 95.01%, 93.82%, and 90.67%; MU, 5893 ± 186, 5825 ± 286, and 5810 ± 396; MCSv, 0.24, 0.16, and 0.13; and LT, 189.21 ± 36.04, 327.69 ± 67.01, and 430.39 ± 114.34 mm. There was a spread in the low-dose region from offsets of =4 cm and the GPR negatively correlated with LT (r = -0.762). There was minimal correlation between GPR and MU or MCSv. Conclusions: In SIMT SRS VMAT plans with an FFF beam from a linear accelerator, target offsets of <4 cm from the isocenter can minimize the volume of the low-dose region receiving 10 Gy or more. During treatment planning, it is important to choose gantry, couch, and collimator angles that minimize LT and thereby improve the GPR. [ABSTRACT FROM AUTHOR]

Purpose: Deep learning‐based virtual patient‐specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning‐based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed. Methods: Overall, 96 volumetric‐modulated arc therapy plans generated for prostate cancer treatment were used. We developed a model (Model 1) that can predict measurement‐based gamma passing rate (GPR) for a treatment plan using data stored as a map reflecting the MLC leaf position at each control point (MLPM) and data of the dose distribution in patient's CT as inputs. The evaluation of the model was based on the mean absolute error (MAE) and Pearson's correlation coefficient (r) between the measured and predicted GPR. For comparison, we also analyzed models trained with the dose distribution in patient's CT alone (Model 2) and with dose distributions recalculated on a virtual phantom CT (Model 3). Results: At the 2%/2 mm criterion, MAE[%] and r for Model 1, Model 2, and Model 3 were 2.32% ± 0.43% and 0.54 ± 0.03, 2.70% ± 0.26%, and 0.32 ± 0.08, and 2.96% ± 0.23% and 0.24 ± 0.22, respectively; at the 3%/3 mm criterion, these values were 1.25% ± 0.05% and 0.36 ± 0.18, 1.57% ± 0.35% and 0.19 ± 0.20, and 1.39% ± 0.32% and 0.17 ± 0.22, respectively. This result showed that Model 1 exhibited the lowest MAE and highest r at both criteria of 2%/2 mm and 3%3 mm. Conclusions: These findings showed that a model that combines the MLPM and dose distribution in patient's CT exhibited a better GPR prediction performance compared with the other two studied models. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70–74 Gy/28–37 fr) at our hospital were used (n  = 55 for training and n  = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans—i.e. preDose and deliDose—with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04–6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00–15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P  = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer. [ABSTRACT FROM AUTHOR]

Purpose: A new development on the RayStation treatment planning system (TPS) allows a plan to be planned by imposing a constraint on the leaf sequencing: all leaves move in the same direction before moving again in the opposite direction to create a succession of sliding windows (SWs). The study aims to investigate this new leaf sequencing, coupled with standard optimization (SO) and multi‐criteria optimization (MCO) and to compare it with the standard sequencing (STD). Methods: Sixty plans were replanned for 10 head and neck cancer patients (two dose levels simultaneously SIB, 56 and 70 Gy in 35 fractions). All plans were compared, and a Wilcoxon signed‐rank test was performed. Pre‐processing QA and metrics of multileaf collimator (MLC) complexity were studied. Results: All methodologies met the dose requirements for the planning target volumes (PTVs) and organs at risk (OARs). SO demonstrates significantly best results for homogeneity index (HI), conformity index (CI), and target coverage (TC). SO‐SW gives best results for PTVs (D98% and D2%) but the differences between techniques are less than 1%. Only the D2%,PTV‐56 Gy is higher with both MCO methods. MCO‐STD offer the best sparing OARs (parotids, spinal cord, larynx, oral cavity). The gamma passing rates (GPRs) with 3%/3 mm criteria between the measured and calculated dose distributions are higher than 95%, slightly lowest with SW. The number of monitor units (MUs) and MLC metrics are higher in SW show a higher modulation. Conclusions: All plans are feasible for the treatment. A clear advantage of SO‐SW is that the treatment plan is more straightforward to planning by the user due to the more advanced modulation. MCO stands out for its ease of use and will allow a less experienced user to offer a better plan than in SO. In addition, MCO‐STD will reduce the dose to the OARs while maintaining good TC. [ABSTRACT FROM AUTHOR]

Background: we assessed the performance of the optimization algorithms by comparing volumetric modulated arc therapy generated by a progressive resolution optimized (VMATPRO) and photon optimizer (VMATPO) in terms of plan quality, MU reduction, sparing of the spinal cord (or cauda equina), and plan complexity. Methods: Fifty-seven patients who received spine stereotactic ablative radiotherapy (SABR) with tumors located in the cervical, thoracic, and lumbar spine were retrospectively selected. For each patient, VMATPRO and VMATPO with two full arcs were generated with using the PRO and PO algorithms. For dosimetric evaluation, the dose-volumetric (DV) parameters of the planning target volume (PTV), organs at risk (OARs), the corresponding planning organs at risk (PRV), and 1.5-cm ring structure surrounding the PTV (Ring1.5 cm) were calculated for all VMAT plans. The total number of monitor units (MUs) and the modulation complexity score for the VMAT (MCSv) were compared. To investigate the correlations of OAR sparing to plan complexity, Pearson’s and Spearman’s correlation tests were conducted between the two algorithms (PO – PRO, denoted as Δ) in the DV parameters for normal tissues, total MUs, and MCSv. Results: For the PTVs, Target conformity and dose homogeneity in the PTVs of VMATPRO were better than those of VMATPO with statistical significance. For the spinal cords (or cauda equine) and the corresponding PRVs, all of the DV parameters for VMATPRO were markedly lower than those for VMATPO, with statistical significance (all p < 0.0001). Among them, the difference in the maximum dose to the spinal cord between VMATPRO and VMATPO was remarkable (9.04 Gy vs. 11.08 Gy with p < 0.0001). For Ring1.5 cm, no significant difference in V115% for VMATPRO and VMATPO was observed. Conclusions: The use of VMATPRO resulted in improved coverage and uniformity of dose to the PTV, as well as OARs sparing, compared with that of VMATPO for cervical, thoracic, and lumbar spine SABR. Better dosimetric plan quality generated by the PRO algorithm was observed to result in higher total MUs and plan complexity. Therefore, careful evaluation of its deliverability should be performed with caution during the routine use of the PRO algorithm. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate knowledge-based treatment planning (KBP) models in terms of their dosimetry and deliverability and to investigate their clinical benefits. Three H&N KBP models were built utilizing RapidPlan™, based on the dose prescription, which is given according to the planning target volume (PTV). The training set for each model consisted of 43 clinically acceptable volumetric modulated arc therapy (VMAT) plans. Model quality was assessed and compared to the delivered treatment plans using the homogeneity index (HI), conformity index (CI), structure dose difference (PTV, organ at risk—OAR), monitor units, MU factor, and complexity index. Model deliverability was assessed through a patient-specific quality assurance (PSQA) gamma index-based analysis. The dosimetric assessment showed better OAR sparing for the RapidPlan™ plans and for the low- and high-risk PTV, and the HI, and CI were comparable between the clinical and RapidPlan™ plans, while for the intermediate-risk PTV, CI was better for clinical plans. The 2D gamma passing rates for RapidPlan™ plans were similar or better than the clinical ones using the 3%/3 mm gamma-index criterion. Monitor units, the MU factors, and complexity indices were found to be comparable between RapidPlan™ and the clinical plans. Knowledge-based treatment plans can be safely adapted into clinical routines, providing improved plan quality in a time efficient way while minimizing user variability. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop an algorithm to predict gamma passing rate (GPR) in the volumetric‐modulated arc therapy (VMAT) technique. Materials and methods: A total of 118 clinical VMAT plans, including 28 mediastina, 25 head and neck, 40 brains intensity‐modulated radiosurgery, and 25 prostate cases, were created in RayStation treatment planning system for Edge and TrueBeam linacs. In‐house scripts were developed to compute Modulation indices such as plan‐averaged beam area (PA), plan‐averaged beam irregularity (PI), total monitor unit (MU), leaf travel/arc length, mean dose rate variation, and mean gantry speed variation. Pretreatment verifications were performed on ArcCHECK phantom with SNC software. GPR was calculated with 3%/2 mm and 10% threshold. The dataset was randomly split into a training (70%) and a test (30%) dataset. A random forest regression (RFR) model and support vector regression (SVR) with linear kernel were trained to predict GPR using the complexity metrics as input. The prediction performance was evaluated by calculating the mean absolute error (MAE), R2, and root mean square error (RMSE). Results: RMSEs at γ 3%/2 mm for RFR and SVR were 1.407 ± 0.103 and 1.447 ± 0.121, respectively. MAE was 1.14 ± 0.084 for RFR and 1.101 ± 0.09 for SVR. R2 was equal to 0.703 ± 0.027 and 0.689 ± 0.053 for RFR and SVR, respectively. GPR of 3%/2 mm with a 10% threshold can be predicted with an error smaller than 3% for 94% of plans using RFR and SVR models. The most important metrics that had the greatest impact on how accurately GPR can be predicted were determined to be the PA, PI, and total MU. Conclusion: In terms of its prediction values and errors, SVR (linear) appeared to be comparable with RFR for this dataset. Based on our results, the PA, PI, and total MU calculations may be useful in guiding VMAT plan evaluation and ultimately reducing uncertainties in planning and radiation delivery. [ABSTRACT FROM AUTHOR]

Purpose: To develop an independent log file–based intensity‐modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance‐linac (MR‐linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced. Methods: The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor‐supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units. Results: The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work. Conclusions: An independent MC and log file–based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to investigate the effects of different multi-leaf collimator (MLC) speed constraints in volumetric modulated radiotherapy (VMAT) on the robustness of treatment plans for central lung cancer patients. Method and Materials: Twenty patients with central lung tumor who underwent stereotactic body radiotherapy (SBRT) with the VMAT technique at our hospital were included in this retrospective study. The reference plans were created with 3 different MLC speed constraints (Plan A: 0.1 cm/deg., Plan B: 0.3 cm/deg., and Plan C: 0.5 cm/deg.) with a 50-Gy/8Fr, planning target volume (PTV) D95% prescription. In each of these plans, setup errors from 1 to 5 mm were intentionally added in the direction of the central organ at 1-mm intervals (300 plans [20 cases × 3 MLC speeds × 5 error plans] were created in total). Each plan was then calculated by the same beam conditions as each reference plan. The actual average MLC speed and dose difference between the reference plan and the error-added plan were then calculated and compared among the 3 MLC speeds. Results: In the reference plans, the actual average MLC speeds were 0.25 ± 0.04, 0.34 ± 0.07, and 0.39 ± 0.12 cm/deg. for Plan A, Plan B, and Plan C, respectively (P <.05). For PTV and OARs, many dose indices tended to improve as the MLC speed increased, while no significant differences were observed among the 3 MLC speed constraints. However, in assessments of robustness, no significant differences in dose difference were observed among the 3 MLC speed constraints for most of the indices. Conclusions: When necessary, increasing the MLC speed constraint with a priority on improving the quality of the dose distribution is an acceptable approach for central lung cancer patients. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop a predictive model for patient‐specific VMAT QA results using multileaf collimator (MLC) effect and texture analysis. The MLC speed, acceleration and texture analysis features were extracted from 106 VMAT plans as predictors. Gamma passing rate (GPR) was collected as a response class with gamma criteria of 2%/2 mm and 3%/2 mm. The model was trained using two machine learning methods: AdaBoost classification and bagged regression trees model. GPR was classified into the "PASS" and "FAIL" for the classification model using the institutional warning level. The accuracy of the model was assessed using sensitivity and specificity. In addition, the accuracy of the regression model was determined using the difference between predicted and measured GPR. For the AdaBoost classification model, the sensitivity/specificity was 94.12%/100% and 63.63%/53.13% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. For the bagged regression trees model, the sensitivity/specificity was 94.12%/91.89% and 61.18%/68.75% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The root mean square error (RMSE) of difference between predicted and measured GPR was found at 2.44 and 1.22 for gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The promising result was found at tighter gamma criteria 2%/2 mm with 94.12% sensitivity (both bagged regression trees and AdaBoost classification model) and 100% specificity (AdaBoost classification model). [ABSTRACT FROM AUTHOR]

Background/Aim: The aim of this study was to evaluate the mechanical performance and the effect on dose distribution and deliverability of volumetric modulated arc therapy (VMAT) plans for prostate cancer created with the commercial knowledge-based planning (KBP) system (RapidPlan™). Materials and Methods: Three institutions, A, B, and C were enrolled in this study. Each institution established and trained a KBP model with their own cases. CT data and structures for 45 patients at institution B were utilized to validate the dose-volume parameters (D2(%), D95(%), and D98(%) for target, and V50(%), V75(%), and V90(%) for rectum and bladder), and the following mechanical performance parameters and gamma passing rates of each KBP model: leaf sequence variability (LSV), aperture area variability (AAV), total monitor unit (MU), modulation complexity score for VMAT (MCSv), MU/control point (CP), aperture area (AA)/CP, and MUxAA/CP. Results: Significant differences (p<0.01) in dosimetric parameters such as D2 and D98 for target and V50, V75, and V90 for bladder were observed among the three institutions. The means and standard deviations of MCSv were 0.31±0.03, 0.29±0.02, and 0.32±0.03, and the angles of maximum and minimum MUxAA/CP were 269° and 13°, 269° and 13°, and 273° and 153° at institutions A, B, and C, respectively. The mean gamma passing rate (1%/1 mm.) was >95% for all cases in each institution. Dose distribution and mechanical performance significantly differed between the three models. Conclusion: Each KBP model had different dose distributions and mechanical performance but could create an acceptable plan for deliverability regardless of mechanical performance. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the utility of a new plan feature (planomics feature) for predicting the results of patient-specific quality assurance using the head and neck (H&N) volumetric modulated arc therapy (VMAT) plan. Methods: One hundred and thirty-one H&N VMAT plans in our institution from 2019 to 2021 were retrospectively collected. Dosimetric verification for all plans was carried out using the portal dosimetry system integrated into the Eclipse treatment planning system based on the electronic portal imaging devices. Gamma passing rates (GPR) were analyzed using three gamma indices of 3%/3 mm, 3%/2 mm, and 2%/2 mm with a 10% dose threshold. Forty-eight conventional features affecting the dose delivery accuracy were used in the study, and 2,476 planomics features were extracted based on the radiotherapy plan file. Three prediction and classification models using conventional features (CF), planomics features (PF), and hybrid features (HF) combining two sets of features were constructed by the gradient boosting regressor (GBR) and Ridge classifier for each GPR of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. The absolute prediction error (APE) and the area under the curve (AUC) were adopted for assessing the performance of prediction and classification models. Results: In the GPR prediction, the average APE of the models using CF, PF, and HF was 1.3 ± 1.2%/3.6 ± 3.0%, 1.7 ± 1.5%/3.8 ± 3.5%, and 1.1 ± 1.0%/4.1 ± 3.1% for 2%/2 mm; 0.7 ± 0.6%/2.0 ± 2.0%, 1.0±1.1%/2.2 ± 1.8%, and 0.6 ± 0.6%/2.2 ± 1.9% for 3%/2 mm; and 0.4 ± 0.3%/1.2 ± 1.2%, 0.4±0.5%/1.3 ± 1.0%, and 0.3±0.3%/1.2 ± 1.1% for 3%/3 mm, respectively. In the regression prediction, three models give a similar modeling performance for predicting the GPR. The classification results were 0.67 ± 0.03/0.66 ± 0.07, 0.77 ± 0.03/0.73 ± 0.06, and 0.78 ± 0.02/0.75 ± 0.04 for 3%/3 mm, respectively. For 3%/2 mm, the AUCs of the training and testing cohorts were 0.64 ± 0.03/0.62 ± 0.07, 0.70 ± 0.03/0.67 ± 0.06, and 0.75 ± 0.03/0.71 ± 0.07, respectively, and for 2%/2 mm, the average AUCs of the training and testing cohorts were 0.72 ± 0.03/0.72 ± 0.06, 0.78 ± 0.04/0.73 ± 0.07, and 0.81 ± 0.03/0.75 ± 0.06, respectively. In the classification, the PF model has a better classification performance than the CF model. Moreover, the HF model provides the best result among the three classifications models. Conclusions: The planomics features can be used for predicting and classifying the GPR results and for improving the model performance after combining the conventional features for the GPR classification. [ABSTRACT FROM AUTHOR]

To optimize the collimator angles in dual-arc volumetric modulated arc therapy (VMAT) plans for whole-brain radiotherapy with hippocampus and inner ear sparing (HIS-WBRT). Two sets of dual-arc VMAT plans were generated for 13 small-cell lung cancer patients: (1) The collimator angles of arcs 1 and 2 (θ1/θ2) were 350°/10°, 350°/30°, 350°/45°, 350°/60°, and 350°/80°, i.e., the intersection angle of θ1 and θ2 (Δθ) increased. (2) θ1/θ2 were 280°/10°, 300°/30°, 315°/45°, 330°/60°, and 350°/80°, i.e., Δθ = 90°. The conformity index (CI), homogeneity index (HI), monitor units (MUs), and dosimetric parameters of organs-at-risk were analyzed. Quality assurance for Δθ = 90° plans was performed. With Δθ increasing towards 90°, a significant improvement was observed for most parameters. In 350°/80° plans compared with 350°/10° ones, CI and HI were improved by 1.1% and 25.2%, respectively; MUs were reduced by 16.2%; minimum, maximum, and mean doses (D100%, Dmax, and Dmean, respectively) to the hippocampus were reduced by 5.5%, 6.3%, and 5.4%, respectively; Dmean to the inner ear and eye were reduced by 0.7% and 5.1%, respectively. With Δθ kept at 90°, the plan quality was not significantly affected by θ1/θ2 combinations. The gamma-index passing rates in 280°/10° and 350°/80° plans were relatively lower compared with the other Δθ = 90° plans. Δθ showed a significant effect on dual-arc VMAT plans for HIS-WBRT. With Δθ approaching 90°, the plan quality exhibited a nearly continuous improvement, whereas with Δθ = 90°, the effect of θ1/θ2 combination was insignificant. [ABSTRACT FROM AUTHOR]

Aim of the study: To evaluate the agreement between planned and delivered doses and its potential correlation with the plans' complexity subjected to dosimetric verification. Material and methods: Four isocentre volumetric modulated arc therapy for total marrow irradiation plans optimized simultaneously with (P1) and without (P2) MU reduction were evaluated dosimetrically by g method performed in a global mode for 4 combinations of γ-index criteria (2%/2 mm, 2%/3 mm, 3%/2 mm, and 3%/3 mm). The evaluation was conducted for 4 regions (head and neck, chest, abdomen and upper pelvis, and lower pelvis and thighs) that were determined geometrically by the isocentres. The Wilcoxon test was used to detect significant differences between g passing rate (GPR) analysis results for the P1 and P2 plans. The Pearson correlation was used to check the relationship between GPR and the plans' complexity. Results: Except for the head and neck region, the P2 plans had better GPRs than the P1 plans. Only for hard combinations of γ-index criteria (i.e. 2%/3 mm, 2%/2 mm) were the GPRs differences between P1 and P2 clinically meaningful, and they were detected in the chest, abdomen and upper pelvis, and lower pelvis and thighs regions. The highest correlations between GPR and the indices describing the plans' complexity were found for the chest region. No correlation was found for the head and neck region. Conclusions: The P2 plans showed better agreement between planned and delivered doses compared to the P1 plans. The GPR and the plans' complexity depend on the anatomy region and are most important for the chest region. [ABSTRACT FROM AUTHOR]

Purpose: To validate an MR‐compatible version of the ScandiDos Delta4 Phantom+ on a 0.35T MR guided linear accelerator (MR‐Linac) system and to determine the effect of plan complexity on the measurement results. Methods/Materials: 36 clinical treatment plans originally delivered on a 0.35T MR linac system were re‐planned on the Delta4 Phantom+ MR geometry following our clinical quality assurance (QA) protocol. The QA plans were then measured using the Delta4 Phantom+ MR and the global gamma pass rates were compared to previous results measured using a Sun Nuclear ArcCHECK‐MR. Both 3%/3mm and 2%/2mm global gamma pass rates with a 20% dose threshold were recorded and compared. Plan complexity was quantified for each clinical plan investigated using 24 different plan metrics and each metric's correlation with the overall 2%/2mm global gamma pass rate was investigated using Pearson correlation coefficients. Results: Both systems demonstrated comparable levels of gamma pass rates at both the 3%/3mm and 2%/2mm level for all plan complexity metrics. Nine plan metrics including area, number of active MLCs, perimeter, edge metric, leaf segment variability, complete irradiation area outline, irregularity, leaf travel index, and unique opening index were moderately (|r| > 0.5) correlated with the Delta4 2%/2mm global gamma pass rates whereas those same metrics had weak correlation with the ArcCHECK‐MR pass rates. Only the perimeter to area ratio and small aperture score (20 mm) metrics showed moderate correlation with the ArcCHECK‐MR gamma pass rates. Conclusions: The MR‐compatible version of the ScandiDos Delta4 Phantom+ MR has been validated for clinical use on a 0.35T MR‐Linac with results being comparable to an ArcCHECK‐MR system in use clinically for almost five years. Most plan complexity metrics did not correlate with lower 2%/2mm gamma pass rates using the ArcCHECK‐MR but several metrics were found to be moderately correlated with lower 2%/2mm global gamma pass rates for the Delta4 Phantom+ MR. [ABSTRACT FROM AUTHOR]

Purpose: Patient‐specific quality assurance (QA) is very important in radiotherapy, especially for patients with highly conformed treatment plans like VMAT plans. Traditional QA protocols for these plans are time‐consuming reducing considerably the time available for patient treatments. In this work, a new MC‐based secondary dose check software (SciMoCa) is evaluated and benchmarked against well‐established TPS (Monaco and Pinnacle3) by means of treatment plans and dose measurements. Methods: Fifty VMAT plans have been computed using same calculation parameters with SciMoCa and the two primary TPSs. Plans were validated with measurements performed with a 3D diode detector (ArcCHECK) by translating patient plans to phantom geometry. Calculation accuracy was assessed by measuring point dose differences and gamma passing rates (GPR) from a 3D gamma analysis with 3%–2 mm criteria. Comparison between SciMoCa and primary TPS calculations was made using the same estimators and using both patient and phantom geometry plans. Results: TPS and SciMoCa calculations were found to be in very good agreement with validation measurements with average point dose differences of 0.7 ± 1.7% and −0.2 ± 1.6% for SciMoCa and two TPSs, respectively. Comparison between SciMoCa calculations and the two primary TPS plans did not show any statistically significant difference with average point dose differences compatible with zero within error for both patient and phantom geometry plans and GPR (98.0 ± 3.0% and 99.0 ± 3.0% respectively) well in excess of the typical 95%clinical tolerance threshold. Conclusion: This work presents results obtained with a significantly larger sample than other similar analyses and, to the authors' knowledge, compares SciMoCa with a MC‐based TPS for the first time. Results show that a MC‐based secondary patient‐specific QA is a clinically viable, reliable, and promising technique, that potentially allows significant time saving that can be used for patient treatment and a per‐plan basis QA that effectively complements traditional commissioning and calibration protocols. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare single-arc (SA) and double-arc (DA) treatment plans, which are planning techniques often used in prostate cancer volumetric modulated arc therapy (VMAT), in the presence of intrafractional deformation (ID) to determine which technique is superior in terms of target dose coverage and sparing of the organs at risk (OARs). SA and DA plans were created for 27 patients with localized prostate cancer. ID was introduced to the clinical target volume (CTV), rectum and bladder to obtain blurred dose distributions using an in-house software. ID was based on the motion probability function of each structure voxel and the intrafractional motion of the respective organs. From the resultant blurred dose distributions of SA and DA plans, various parameters, including the tumor control probability, normal tissue complication probability, homogeneity index, conformity index, modulation complexity score for VMAT, dose–volume indices and monitor units (MUs), were evaluated to compare the two techniques. Statistical analysis showed that most CTV and rectum parameters were significantly larger for SA plans than for DA plans (P  < 0.05). Furthermore, SA plans had fewer MUs and were less complex (P  < 0.05). The significant differences observed had no clinical significance, indicating that both plans are comparable in terms of target and OAR dosimetry when ID is considered. The use of SA plans is recommended for prostate cancer VMAT because they can be delivered in shorter treatment times than DA plans, and therefore benefit the patients. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate the correlations of the gamma passing rates (GPR) with the dose-volumetric parameter changes between the original volumetric modulated arc therapy (VMAT) plans and the actual deliveries of the VMAT plans (DV errors). We compared the correlations of the TrueBeam STx system to those of a C-series linac. Methods: A total of 20 patients with head and neck (H&N) cancer were retrospectively selected for this study. For each patient, two VMAT plans with the TrueBeam STx and Trilogy (C-series linac) systems were generated under similar modulation degrees. Both the global and local GPRs with various gamma criteria (3%/3 mm, 2%/2 mm, 2%/1 mm, 1%/2 mm, and 1%/1 mm) were acquired with the 2D dose distributions measured using the MapCHECK2 detector array. During VMAT deliveries, the linac log files of the multi-leaf collimator positions, gantry angles, and delivered monitor units were acquired. The DV errors were calculated with the 3D dose distributions reconstructed using the log files. Subsequently, Spearman's rank correlation coefficients (rs) and the corresponding p values were calculated between the GPRs and the DV errors. Results: For the Trilogy system, the rs values with p < 0.05 showed weak correlations between the GPRs and the DV errors (rs<0.4) whereas for the TrueBeam STx system, moderate or strong correlations were observed (rs≥0.4). The DV errors in the V20Gy of the left parotid gland and those in the mean dose of the right parotid gland showed strong correlations (always with rs > 0.6) with the GPRs with gamma criteria except 3%/3 mm. As the GPRs increased, the DV errors decreased. Conclusion: The GPRs showed strong correlations with some of the DV errors for the VMAT plans for H&N cancer with the TrueBeam STx system. [ABSTRACT FROM AUTHOR]

Volumetric‐modulated arc therapy (VMAT) treatment plans that are highly modulated or complex may result in disagreements between the planned dose distribution and the measured dose distribution. This study investigated established VMAT complexity metrics as a means of predicting phantom‐based measurement results for 93 treatments delivered on a TrueBeam linac, and 91 treatments delivered on two TrueBeam STx linacs. Complexity metrics investigated showed weak correlations to gamma passing rate, with the exception of the Modulation Complexity Score for VMAT, yielding moderate correlations. The Spearman's rho values for this metric were 0.502 (P < 0.001) and 0.528 (P < 0.001) for the TrueBeam and TrueBeam STx, respectively. Receiver operating characteristic analysis was also performed. The aperture irregularity on the TrueBeam achieved a 53% true positive rate and a 9% false‐positive rate to correctly identify complex plans. Similarly, the average field width on the TrueBeam STx achieved a 60% true‐positive rate and an 8% false‐positive rate. If incorporated into clinical workflow, these thresholds can identify highly modulated plans and reduce the number of dose verification measurements required. [ABSTRACT FROM AUTHOR]

Interplay effects in highly modulated stereotactic body radiation therapy lung cases treated with volumetric modulated arc therapy. Purpose: To evaluate the influence of tumor motion on dose delivery in highly modulated stereotactic body radiotherapy (SBRT) of lung cancer using volumetric modulated arc therapy (VMAT). Methods: 4D‐CT imaging data of the quasar respiratory phantom were acquired, using a GE Lightspeed 16‐slice CT scanner, while the phantom reproduced patient specific respiratory traces. Flattening filter‐free (FFF) dual‐arc VMAT treatment plans were created on the acquired images in Pinnacle3 treatment planning system. Each plan was generated with varying levels of complexity characterized by the modulation complexity score. Static and dynamic measurements were delivered to GafChromic EBT3 film inside the respiratory phantom using an Elekta Versa HD linear accelerator. The treatment prescription was 10 Gy per fraction for 5 fractions. Comparisons of the planned and delivered dose distribution were performed using Radiological Imaging Technology (RIT) software. Results: For the motion amplitudes and periods studied, the interplay effect is insignificant to the GTV coverage. The mean dose deviations between the planned and delivered dose distribution never went below −2.00% and a minimum dose difference of −5.05% was observed for a single fraction. However for amplitude of 2 cm, the dose error could be as large as 20.00% near the edges of the PTV at increased levels of complexity. Additionally, the modulation complexity score showed an ability to provide information related to dose delivery. A correlation value (R) of 0.65 was observed between the complexity score and the gamma passing rate for GTV coverage. Conclusions: As expected, respiratory motion effects are most evident for large amplitude respirations, complex fields, and small field margins. However, under all tested conditions target coverage was maintained. [ABSTRACT FROM AUTHOR]

Purpose: To validate the clinical applicability of knowledge‐based (KB) planning in single‐isocenter volumetric‐modulated arc therapy (VMAT) for multiple brain metastases using the k‐fold cross‐validation (CV) method. Methods: This study comprised 60 consecutive patients with multiple brain metastases treated with single‐isocenter VMAT (28 Gy in five fractions). The patients were divided randomly into five groups (Groups 1–5). The data of Groups 1–4 were used as the training and validation dataset and those of Group 5 were used as the testing dataset. Four KB models were created from three of the training and validation datasets and then applied to the remaining Groups as the fourfold CV phase. As the testing phase, the final KB model was applied to Group 5 and the dose distributions were calculated with a single optimization process. The dose‐volume indices (DVIs), modified Ian Paddick Conformity Index (mIPCI), modulation complexity scores for VMAT plans (MCSv), and the total number of monitor units (MUs) of the final KB plan were compared to those of the clinical plan (CL) using a paired Wilcoxon signed‐rank test. Results: In the fourfold CV phase, no significant differences were observed in the DVIs among the four KB plans (KBPs). In the testing phase, the final KB plan was statistically equivalent to the CL, except for planning target volumes (PTVs) D2% and D50%. The differences between the CL and KBP in terms of the PTV D99.5%, normal brain, and Dmax to all organs at risk (OARs) were not significant. The KBP achieved a lower total number of MUs and higher MCSv than the CL with no significant difference. Conclusions: We demonstrated that a KB model in a single‐isocenter VMAT for multiple brain metastases was equivalent in dose distribution, MCSv, and total number of MUs to a CL with a single optimization. [ABSTRACT FROM AUTHOR]

Purpose: A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality. Methods: Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination. Results: The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths. Conclusions: This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality. [ABSTRACT FROM AUTHOR]

Background: Total scalp irradiation presents technical and dosimetric challenges. While reports suggest that HyperArc, a new stereotactic radiosurgery planning technique applied to non-coplanar volumetric-modulated arc therapy (VMAT) technique, is associated with high conformity and rapid dose fall-off, the performance of HyperArc for total scalp irradiation has not been explored. The current study aimed to compare the dosimetric performance of HyperArc plans with those of non-coplanar VMAT plans in angiosarcoma of the scalp.Methods: Ten patients with angiosarcoma of the scalp were included in this study. The performance of three different plans administered using TrueBeam Edge were compared: non-coplanar VMAT using flattening filter (FF) beams (VMAT-FF), HyperArc using FF beams (HyperArc-FF), and HyperArc using flattening filter free (FFF) beams (HyperArc-FFF). The dose distribution, dosimetric parameters, and dosimetric accuracy for each of these plans were evaluated.Results: The three plans showed no statistically significant differences in target volume coverage, conformity, and homogeneity. The HyperArc-FF and HyperArc-FFF plans provided significantly lower mean brain doses (12.63 ± 3.31 Gy and 12.71 ± 3.40 Gy) than did the VMAT-FF plans (17.11 ± 5.25 Gy). There were almost no differences in sparing the organs at risk between the HyperArc-FF and HyperArc-FFF plans. The HyperArc-FF and HyperArc-FFF plans provided a shorter beam-on time than did the VMAT-FF plan. The 3%/2 mm gamma test pass rates were above 95% for all three plans.Conclusions: Our results suggest that the HyperArc plan can be potentially used for radiation therapy of target regions with large and complicated shapes, such as the scalp, and that there are no advantages of using FFF beams. [ABSTRACT FROM AUTHOR]

We investigated the feasibility of a robust optimization with 6 MV X-ray (6X) and 10 MV X-ray (10X) flattening filter-free (FFF) beams in a volumetric modulated arc therapy (VMAT) plan for lung stereotactic body radiation therapy (SBRT) using a breath-holding technique. Ten lung cancer patients were selected. Four VMAT plans were generated for each patient; namely, an optimized plan based on the planning target volume (PTV) margin and a second plan based on a robust optimization of the internal target volume (ITV) with setup uncertainties, each for the 6X- and 10X-FFF beams. Both optimized plans were normalized by the percentage of the prescription dose covering 95% of the target volume (D95%) to the PTV (1050 cGy × 4 fractions). All optimized plans were evaluated using perturbed doses by specifying user-defined shifted values from the isocentre. The average perturbed D99% doses to the ITV, compared to the nominal plan, decreased by 369.1 (6X-FFF) and 301.0 cGy (10X-FFF) for the PTV-based optimized plan, and 346.0 (6X-FFF) and 271.6 cGy (10X-FFF) for the robust optimized plan, respectively. The standard deviation of the D99% dose to the ITV were 163.6 (6X-FFF) and 158.9 cGy (10X-FFF) for the PTV-based plan, and 138.9 (6X-FFF) and 128.5 cGy (10X-FFF) for the robust optimized plan, respectively. Robust optimized plans with 10X-FFF beams is a feasible method to achieve dose certainty for the ITV for lung SBRT using a breath-holding technique. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate a robust template for VMAT SBRT of lung lesions, using the multicriterial optimization (MCO) of a commercial treatment planning system. Methods: The template was established and refined on 10 lung SBRT patients planned for 55 Gy/5 fr. To improve gradient and conformity a ring structure around the planning target volume (PTV) was set in the list of objectives. Ideal fluence optimization was conducted giving priority to organs at risk (OARs) and using the MCO, which further pushes OARs doses. Segmentation was conducted giving priority to PTV coverage. Two different templates were produced with different degrees of modulation, by setting the Fluence Smoothing parameter to Medium (MFS) and High (HFS). Each template was applied on 20 further patients. Automatic and manual plans were compared in terms of dosimetric parameters, delivery time, and complexity. Statistical significance of differences was evaluated using paired two‐sided Wilcoxon signed‐rank test. Results: No statistically significant differences in PTV coverage and maximum dose were observed, while an improvement was observed in gradient and conformity. A general improvement in dose to OARs was seen, which resulted to be significant for chest wall V30 Gy, total lung V20 Gy, and spinal cord D0.1 cc. MFS plans are characterized by a higher modulation and longer delivery time than manual plans. HFS plans have a modulation and a delivery time comparable to manual plans, but still present an advantage in terms of gradient. Conclusion: The automation of the planning process for lung SBRT using robust templates and MCO was demonstrated to be feasible and more efficient. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate whether knowledge-based plans (KBP) from a single optimization could be used clinically, and to compare dose–volume histogram (DVH) parameters and plan quality between KBP with (KBPCONST) and without (KBPORIG) manual objective constraints and clinical manual optimized (CMO) plans for pharyngeal cancer. KBPs were produced from a system trained on clinical plans from 55 patients with pharyngeal cancer who had undergone intensity-modulated radiation therapy or volumetric-modulated arc therapy (VMAT). For another 15 patients, DVH parameters of KBPCONST and KBPORIG from a single optimization were compared with CMO plans with respect to the planning target volume (D98%, D50%, D2%), brainstem maximum dose (Dmax), spinal cord Dmax, parotid gland median and mean dose (Dmed and Dmean), monitor units and modulation complexity score for VMAT. The Dmax of spinal cord and brainstem and the Dmed and Dmean of ipsilateral parotid glands were unacceptably high for KBPORIG, although the KBPCONST DVH parameters met our goal for most patients. KBPCONST and CMO plans produced comparable DVH parameters. The monitor units of KBPCONST were significantly lower than those of the CMO plans (P  < 0.001). Dose distribution of the KBPCONST was better than or comparable to that of the CMO plans for 13 (87%) of the 15 patients. In conclusion, KBPORIG was found to be clinically unacceptable, while KBPCONST from a single optimization was comparable or superior to CMO plans for most patients with head and neck cancer. [ABSTRACT FROM AUTHOR]

A thorax phantom was used to assess radiotherapy dose deviations induced by respiratory motion of the target volume. Both intensity modulated and static, non‐modulated treatment plans were planned on CT scans of the phantom. The plans were optimized using various CT reconstructions, to investigate whether they had an impact on robustness to target motion during delivery. During irradiation, the target was programmed to simulate respiration‐induced motion of a lung tumor, using both patient‐specific and sinusoidal motion patterns in three dimensions. Dose was measured in the center of the target using an ion chamber. Differences between reference measurements with a stationary target and dynamic measurements were assessed. Possible correlations between plan complexity metrics and measured dose deviations were investigated. The maximum observed motion‐induced dose differences were 7.8% and 4.5% for single 2 Gy and 15 Gy fractions, respectively. The measurements performed with the largest target motion amplitude in the superior–inferior direction yielded the largest dosimetric deviations. For 2 Gy fractionation schemes, the summed dose deviation after 33 fractions is likely to be less than 2%. Measured motion‐induced dose deviations were significantly larger for one CT reconstruction compared to all the others. Static, non‐modulated plans showed superior robustness to target motion during delivery. Moderate correlations between the modulation complexity score applied to VMAT (MCSv) and measured dose deviations were found for 15 Gy SBRT treatment plans. Correlations between other plan complexity metrics and measured dose deviations were not found. [ABSTRACT FROM AUTHOR]

This study constitutes a feasibility assessment of dynamic conformal arc (DCA) therapy as an alternative to volumetric‐modulated arc therapy (VMAT) for stereotactic body radiation therapy (SBRT) of lung cancer. The rationale for DCA is lower geometric complexity and hence reduced risk for interplay errors induced by respiratory motion. Forward planned DCA and inverse planned DCA based on segment‐weight optimization were compared to VMAT for single arc treatments of five lung patients. Analysis of dose‐volume histograms and clinical goal fulfillment revealed that DCA can generate satisfactory and near equivalent dosimetric quality to VMAT, except for complex tumor geometries. Segment‐weight optimized DCA provided spatial dose distributions qualitatively similar to those for VMAT. Our results show that DCA, and particularly segment‐weight optimized DCA, may be an attractive alternative to VMAT for lung SBRT treatments if the patient anatomy is favorable. [ABSTRACT FROM AUTHOR]